Nick Chamandy Nick Chamandy on

Simple “random-user” A/B experiment designs fall short in the face of complex dependence structures. These can come in the form of large-scale social graphs or, more recently, spatio-temporal network interactions in a two-sided transportation marketplace. Naive designs are susceptible to statistical interference, which can lead to biased estimates of the treatment effect under study.

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Amir Najmi Amir Najmi on

Scalable web technology has greatly reduced the marginal cost of serving users. Thus, an individual business today may support a very large user base. With so much data, one might imagine that it is easy to obtain statistical significance in live experiments. However, this is always not the case. Often, the very business models enabled by the web require answers for which our data is information poor.

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Christian Posse Christian Posse on

christian posse a/b testingDr. Christian Posse was the last panelist at the recent The Hive Big Data Think Tank meetup at Microsoft. In this talk, Christian shares some of the problems he's seen in the social network field. Not a single piece of code, algorithm, feature, or user experience goes out without A/B Testing. He discusses their development of a system of hashing functions over at LinkedIn that allow them to run millions of A/B tests concurrently without interactions between them.


Bio:
Dr. Christian Posse recently joined Google as Program Manager, Technology. Before that he was Principal Product Manager and Principal Data Scientist at LinkedIn where he led the development of recommendation products as well as the next generation online experimentation platform. Prior to LinkedIn, Dr. Posse was a founding member and technology lead of Cisco Systems Network Collaboration Business Unit where he designed the search and advanced social analytics of Pulse, Cisco’s network-based search and collaboration platform for the enterprise. Prior to Cisco, Dr. Posse worked in a wide range of environments, from holding faculty positions in US universities, to leading the R&D at software companies and a US National Laboratory in the social networks, biological networks and behavioral analytics fields. His interests are diverse and include search and recommendation engines, social networks analytics, computational social and behavioral sciences, online experimentation and information fusion. He has written over 40 scientific peer-reviewed publications and holds several patents in those fields. Dr. Posse has a PhD in Statistics from the Swiss Federal Institute of Technology, Switzerland.

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Caitlin Smallwood Caitlin Smallwood on

Controlled Experimentation (or A/B testing) has evolved into a powerful tool for driving product strategy and innovation. The dramatic growth in online and mobile content, media, and commerce has enabled companies to make principled data-driven decisions. Large numbers of experiments are typically run to validate hypotheses, study causation, and optimize user experience, engagement, and monetization.

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